Combining Optimization and Machine Learning for the Formation of Collectives

This thesis considers the problem of forming collectives of agents for real-world applications aligned with Sustainable Development Goals (e.g., shared mobility and cooperative learning). Such problems require fast approaches that can produce solutions of high quality for hundreds of agents. With this goal in mind, existing solutions for the formation of collectives focus on enhancing the optimization approach by exploiting the characteristics of a domain. However, the resulting approaches rely on specific domain knowledge and are not transferable to other collective formation problems. Therefore, approaches that can be applied to various problems need to be studied in order to obtain general approaches that do not require prior knowledge of the domain. Along these lines, this thesis proposes a general approach for the formation of collectives based on a novel combination of machine learning and an \emph{Integer Linear Program}. More precisely, a machine learning component is trained to generate a set of promising collectives that are likely to be part of a solution. Then, such collectives and their corresponding utility values are introduced into an \emph{Integer Linear Program} which finds a solution to the collective formation problem. In that way, the machine learning component learns the structure shared by ``good'' collectives in a particular domain, making the whole approach valid for various applications. In addition, the empirical analysis conducted on two real-world domains (i.e., ridesharing and team formation) shows that the proposed approach provides solutions of comparable quality to state-of-the-art approaches specific to each domain. Finally, this thesis also shows that the proposed approach can be extended to problems that combine the formation of collectives with other optimization objectives. Thus, this thesis proposes an extension of the collective formation approach for assigning pickup and delivery locations to robots in a warehouse environment. The experimental evaluation shows that, although it is possible to use the collective formation approach for that purpose, several improvements are required to compete with state-of-the-art approaches. Overall, this thesis aims to demonstrate that machine learning can be successfully intertwined with classical optimization approaches for the formation of collectives by learning the structure of a domain, reducing the need for ad-hoc algorithms devised for a specific application

Animal health and welfare planning in organic dairy cattle farms

Continuous development is needed within the farm to reach the goal of good animal health and welfare in organic livestock farming. The very different conditions between countries call for models that are relevant for different farming types and can be integrated into local practice and be relevant for each type of farming context. This article reviews frameworks, principles and practices for animal health and welfare planning which are relevant for organic livestock farming. This review is based on preliminary analyses carried out within a European project (acronym ANIPLAN) with participants from seven countries. The process begins with gathering knowledge about the current status within a given herd as background for making decisions and planning future improvements as well as evaluating already implemented improvements. Respectful communication between the owner of the herd and other farmers as well as animal health and welfare professionals (veterinarians and advisors) is paramount. This paper provides an overview of some current animal health and welfare planning initiatives and explains the principles of animal health and welfare planning which are being implemented in ANIPLAN partner countries, in collaboration with groups of organic farmers and organisations

Exploring Restart Distributions

We consider the generic approach of using an experience memory to help exploration by adapting a restart distribution. That is, given the capacity to reset the state with those corresponding to the agent's past observations, we help exploration by promoting faster state-space coverage via restarting the agent from a more diverse set of initial states, as well as allowing it to restart in states associated with significant past experiences. This approach is compatible with both on-policy and off-policy methods. However, a caveat is that altering the distribution of initial states could change the optimal policies when searching within a restricted class of policies. To reduce this unsought learning bias, we evaluate our approach in deep reinforcement learning which benefits from the high representational capacity of deep neural networks. We instantiate three variants of our approach, each inspired by an idea in the context of experience replay. Using these variants, we show that performance gains can be achieved, especially in hard exploration problems.Comment: RLDM 201

Innovation and communication technologies + Problem based learning: a new approach for teaching architecture

This article presents the results obtained during its first year of application in the educational innovation project called “New frameworks of teaching: ICT applied to problem based learning in technical bachelors” (PIE 15-166) developed at the School of Architecture in the University of Malaga. This has been focused on the development of educational strategies based on exploiting the potential of ICT, taking as a framework the ABP. Its application on subjects from different areas of knowledge (architectural composition, urban planning, projects and architectural constructions) has allowed assessing the adaptability of this methodology depending on the content. Among the obtained results can be highlighted the improvement in cross curricular coordination between subjects from different fields of studies, providing different ways of synchronous and asynchronous communication between students and teachers to generate a greater interaction between all the involved subjects; increasing in addition the interest and an improvement of the results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Planning for better animal health and welfare, Report from the 1st ANIPLAN project workshop, Hellevad, October 2007

’Minimising medicine use in organic dairy herds through animal health and welfare planning’, ANIPLAN, is a CORE-Organic project which was initiated in June 2007. The main aim of the project is to investigate active and well planned animal health and welfare promotion and disease prevention as a means of minimising medicine use in organic dairy herds. This aim will be met through the development of animal health and welfare planning principles for organic dairy farms under diverse conditions based on an evaluation of current experiences. This also includes application of animal health and welfare assessment across Europe. In order to bring this into practice the project also aims at developing guidelines for communication about animal health and welfare promotion in different settings, for example, as part of existing animal health advisory services or farmer groups such as the Danish Stable School system and the Dutch network programme. The project is divided into the following five work packages, four of which comprise research activities with the other focused on coordination and knowledge transfer, through meetings, workshops and publications. These proceedings represent our first results in terms of presented papers and discussions at our first project workshop in Hellevad Vandmølle as well as a review of Animal Health Planning in UK. The content of the workshop proceedings reflect the aim and starting points of all work packages, both in terms of analyses prior to the workshop, and developments during the workshop emanating from group work. Besides a general introduction to the project and the ideas of the project, Christoph Winckler provides an overview of the use of animal based parameters based on the results of the WelfareQuality project. Christopher Atkinson and Madeleine Neale presented concepts, principles and the practicalities of Animal Health Planning and Animal Health Plans based on UK experiences. Pip Nicholas from The University of Wales, Aberystwyth produced a report reviewing the current use of animal health and welfare planning. The entire document is included in these workshop proceedings. This was supplemented through presentations from all countries regarding animal health and welfare planning processes and research. These are summarised together with the concepts developed through dialogue at the workshop in the paper by Nicholas, Vaarst and Roderick. Finally, the Danish Stable School principles were presented by Mette Vaarst followed by discussion on different approaches of communication in farmer groups and at the individual level between farmers and advisors. One important outcome from this workshop is a set of preliminary principles for a good health planning process. We concluded through group discussions followed by a plenary session that a health planning process should aim at continuous development and improvement, and should incorporate health promotion and disease handling, based on a strategy where the current situation is evaluated and form basis for action, which is then reviewed in a new evaluation. It is important that any health plan is farm specific and based on farmer ownership, although an external person(s) should be involved, as well as external knowledge. The organic principles should form the framework for any action (meaning that a systems approach is needed), and the plan should be written. The good and positive aspects on each farm – things that other farmers potentially can learn from. The work and studies in dairy farms within the project will be based on these principles and comprise evaluation and review using animal based parameters as well as finding ways of communication with farmers about animal health and welfare